Journal of Applied Science & Process Engineering

An Insight into Water and Temperature Management in Unitised Regenerative Fuel Cell (URFC) during Mode Change

2025-05-07T14:22:18+08:00

Although water-related issues are no stranger to conventional fuel cells, unitised regenerative fuel cells (URFC) sustain amplified effects from this condition due to their transition states. Fuel cell (FC) mode start-ups post water electrolyser (WE) operations suffer significantly due to flooding. Past studies validated the significance of water and heat distribution towards the dynamic response of URFC. Due to complications involved in the numerical study of mode change conditions, this paper suggests the basic procedures required for numerical analysis of the WE to FC mode conversion in a URFC where the final result of each mode is taken as the initial result for the next one. Water removal through gas purging is currently one of the best methods to reduce transient time and increase FC start-up efficiency. However, crucial purging conditions such as operating current density, temperature and purging period play an important role in the successful transition. Lower operating current density, ranging below 0.02A/cm² is reported to have a smoother transition compared to current density above 0.12A/cm². Gas purge relative humidity is only effective up to 4% at the anode and poses no effect during a severe flooding condition. Furthermore, the temperature has the lowest response towards the cell heat source, increasing the transient period. The cell experiences high WE mode efficiency at 80˚C, but it suffers significant catalytic loss. The insight will provide a more profound comprehension of water management during WE mode and a suitable administrative method to achieve smooth FC start-ups.

Determination of Some Metal Ions in Blood by AAS and Assessment of Their Toxicity in Exudative Lung Disorder Patients

2025-07-01T09:31:53+08:00

Metal-induced respiratory diseases are not well documented in Bangladesh. The objective of this study is to assess metal toxicity in terms of concentration levels in exudative lung disorder patients. After acid digestion of blood collected from exudative lung disorder patients, the concentration of eight elements (Cd, Cr, Cu, Pb, Mn, Ni, Fe and Zn) was measured using AAS. The age of the exudative lung disorder patients of both genders ranged from 20 to 75 years, living in urban and rural areas from 11 districts of the Chittagong division. Patients were categorized into three groups: smokers, nonsmokers and former smokers. The role of smoking in the metal toxicity of exudative lung disorder patients was also investigated. Blood samples were collected from healthy persons aged 20-35 years. They were used as a control to compare the metal status of patients. It is shown that current smokers with lung diseases have lower Zn levels in their blood than the patients of former smokers. Linear regression analysis for Ni and Fe in the blood of smokers showed a significant correlation between Fe and Ni at p=0.008 and p=0.003. Correlation of Mn was insignificant at p=0.371, which clearly indicates that smoking may not be a probable factor for increasing Mn in blood. But the level of Fe and Mn in the blood of nonsmokers showed a strong and positive correlation with the coefficient value of 0.814 (p<0.001). The investigation showed that metal toxicity is caused more by breathing polluted air from fuel combustion in industries and vehicles than by smoking.

Localisation of Forest Fires by RFID Sensors Based on RSSI/AoA Combined with ANN

2025-09-03T11:09:05+08:00

Every year, African countries face the tragic loss of life and destruction of natural and personal property due to forest fires. This issue has been the subject of research for many years in an effort to find a solution. This article aims to study the application of 3D multilateration positioning based on a hybrid of received signal strength indicator (RSSI) and angle of arrival (AoA) (RSSI/AoA) using an artificial neural network (ANN) to optimise the position of Radio Frequency Identification (RFID) sensors for forest fire prevention/detection. The first approach is based on the most commonly used radio measurement techniques, such as the hybrid RSSI/AoA technique based on the linear least squares (LLS) method to find a solution that minimises the error in the position of the RFID reader. The second approach presents a method using an RNA to correct the observed RSSI/AoA measurements, thereby aiming to locate RFID sensors in forests where obstacles are present and may influence signals. The simulation results of the RNN model show the best performance, achieving a location error of 0.2208m using four RFID sensors. This research highlights the importance of selecting artificial intelligence models for monitoring forest fires around the world.

Basic Properties of Concrete with Silica Fume as Supplementary Cementitious Material: Effects of Replacement Level and Particle Size

2025-10-02T15:30:12+08:00

This study systematically investigates both the effects of replacement level and particle size of silica fume (SF) on concrete, identifying critical insights for optimising its use as a supplementary cementitious material (SCM). The key finding is that while SF significantly enhances mechanical properties, its optimal performance is contingent on two distinct factors: a specific replacement percentage for different strengths and a refined particle size for overall efficacy. Specifically, compressive strength was maximised at a 20%wt cement replacement, achieving 49.5 MPa at 56 days, whereas flexural strength peaked at a lower 10%wt replacement, showing a 40% increase over the control. This divergence underscores distinct strengthening mechanisms; compressive strength is governed by enhanced bulk hydration, while flexural strength is more sensitive to the densification of the interfacial transition zone (ITZ). Concurrently, any incorporation of SF markedly reduced workability, with slump values plummeting from 178 mm for the control mix to just 25 mm at 25%wt replacement, primarily due to its fine particle morphology. Beyond replacement level, particle size was identified as a decisive factor. Grinding SF from a median diameter of 76 µm to a finer median diameter of 47 µm profoundly improved concrete performance, leading to a 25% increase in early compressive strength and a remarkable more than 60% increase in flexural strength compared to mixes with larger, unground SF particles, despite a manageable reduction in slump. These results demonstrate that the sustainability and structural efficiency gains from using SF are not inherent but must be engineered. Ultimately, successfully balancing the often-competing demands of workability and strength requires a tailored approach that simultaneously optimises both its proportion in the mix and its physical fineness.

Hybrid Block Method for Numerical Solution of First Order Ordinary Differential Equations

2025-10-06T10:41:48+08:00

This research introduces a novel single-step hybrid block method with four intra-step points that attains six-order accuracy, ensures A-stability, consistency, and convergence, and provides an efficient, accurate, and computationally economical tool for solving first-order ordinary differential equations. The formulation incorporates interpolation techniques to approximate function values at points where terms are not explicitly defined on the computational grid. In addition to the construction of the scheme, the paper rigorously investigates its theoretical properties. The results obtained show that the method not only achieves high accuracy but also performs competitively when compared with other established numerical techniques reported in the literature.

Performance of Turbofan Engine Nacelle Design for Boeing 777X Using Computational Fluid Dynamics Analysis

2025-09-28T20:20:58+08:00

The efficient design of turbofan engine nacelles is critical for enhancing aircraft performance and supporting sustainable aviation goals. This study investigates the aerodynamic and thermal performance of various nacelle configurations for the Boeing 777X GE9x engine, focusing on innovative cooling strategies and drag reduction. Using Computational Fluid Dynamics simulations, nacelle shapes of varying lengths (10 m and 5.5 m), including long and short nacelles with and without chevrons, as well as an optimized ultra-short nacelle, were analyzed under cruise conditions. Models were developed using MATLAB and SolidWorks, and simulations were performed in ANSYS Fluent. Results indicate that the long nacelle with chevrons provided the best overall thermal and aerodynamic performance among the conventional designs, reducing drag and block fuel consumption by 10.13%. However, the optimized ultra-short nacelle, developed using a hybrid NSGA-Non Dominated Sorting Genetic Algorithm II and fmincon- Find Minimum of Constrained optimization approach using MATLAB, achieved a significantly lower drag coefficient and reduced block fuel consumption by 80.13%. These findings demonstrate the potential of advanced nacelle designs to improve heat dissipation, reduce aerodynamic drag, and lower emissions, aligning with stringent EASA standards and contributing to sustainable aviation advancements.

Landslide Susceptibility Mapping of Western Sarawak via Artificial Neural Network

2025-04-21T15:58:44+08:00

Landslides are the third most frequent form of natural disaster in Malaysia, following floods and storms. It can cause significant damage to anything in its path, depending on the size and velocity of its debris. Due to the danger that it poses, determining the susceptibility of an area to landslides is a crucial step in risk mitigation. Landslide occurrences are dependent on the numerous environmental variables, which can provide information on the level of susceptibility of other locations with similar variables. To quantify the significance of each variable to landslide occurrence, a supervised Machine Learning model – an Artificial Neural Network was developed for this study. Furthermore, landslide occurrences have been associated with the disturbance of natural slopes to accommodate development, which was the main reason behind the selection of Western Sarawak as the area of interest in this study. The model was developed to understand and make landslide susceptibility predictions based on aspect, curvature, elevation, lithology type, rainfall intensity, slope angle, soil type, and TWI. Evaluating the area under the curve score and recall for the model revealed that, based on the available inputs, the model performed well with a score of 1 and 0.99, respectively.

Optimizing the Coagulation Dose Considering Multi Factors: A Design of Experiment Approach

2025-10-15T15:30:04+08:00

Optimizing coagulant dosage for drinking water treatment is essential for enhancing water quality. It also improves operational efficiency and cost-effectiveness. Traditionally, treatment plants focus on removing turbidity, often neglecting other critical factors such as co-pollutant removal, residual coagulant levels, and sludge production. This study addresses these limitations by optimizing coagulant dosage to simultaneously maximize turbidity and chemical oxygen demand (COD) removal, minimize residual Al concentrations, and reduce sludge generation. It employs a multi–parameter approach to improve the water treatment process, targeting low (10 NTU), medium (50 NTU), and high (400 NTU) turbidity synthetic water samples, representative of Mahaweli River water quality. The methodology includes preparing synthetic water, conducting jar tests to evaluate coagulation performance, and using design of experiments with Response Surface Methodology to identify optimal coagulant dosages and mixing speeds. Poly-aluminum chloride (PAC) was found to be the most effective coagulant for low- and medium-turbidity waters, with optimal dosages of 7 mg/l and 7.8 mg/l, and mixing speeds of 220 rpm and 216 rpm. Under these conditions, the final turbidity of water was 0.1648 NTU and 0.6890 NTU, with sludge weights of 0.0047 g and 0.0382 g, respectively. For high turbidity water, alum was optimal at 27 mg/l, with a mixing speed of 226 rpm, resulting in a turbidity of 2.3904 NTU and a sludge weight of 0.2203 g. COD removal percentages for low, medium, and high turbidity samples were 49.12%, 53.45%, and 49.57%. Residual aluminum levels remained below 10 ppm across all samples, measured via titration and Atomic Absorption Spectroscopy (AAS). These findings show that optimized coagulant dosage improves water quality, reduces sludge, and minimizes chemical residuals, providing cost-effective and sustainable improvements in water treatment. The study recommends multi–parameter optimization strategies and mechanical mixing methods in conventional water treatment plants to enhance efficiency and ensure high-quality drinking water.